Combined shape, appearance and silhouette for simultaneous manipulator and object tracking
Abstract
This paper develops an estimation framework for sensor-guided manipulation of a rigid object via a robot arm. Using an unscented Kalman Filter (UKF), the method combines dense range information (from stereo cameras and 3D ranging sensors) as well as visual appearance features and silhouettes of the object and manipulator to track both an object-fixed frame location as well as a manipulator tool or palm frame location. If available, tactile data is also incorporated. By using these different imaging sensors and different imaging properties, we can leverage the advantages of each sensor and each feature type to realize more accurate and robust object and reference frame tracking. The method is demonstrated using the DARPA ARM-S system, consisting of a Barrett™WAM manipulator.
Additional Information
© 2012 IEEE. Date of Current Version: 28 June 2012. The author gratefully acknowledges the support from the National Science and Engineering Research Council of Canada (NSERC). The research described in this publication was carried out at the Jet Propulsion Laboratory, California Institute of Technology, with funding from the DARPA Autonomous Robotic Manipulation Software Track (ARM-S) program and the U.S. Army under the Robotics Collaborative Technology Alliance through an agreement with NASA.Additional details
- Eprint ID
- 32242
- DOI
- 10.1109/ICRA.2012.6225084
- Resolver ID
- CaltechAUTHORS:20120703-081044046
- National Science and Engineering Research Council of Canada (NSERC)
- Defense Advanced Research Projects Agency (DARPA)
- U. S. Army Robotics Collaborative Technology Alliance
- NASA/JPL/Caltech
- Created
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2012-07-06Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field